Recent years have witnessed renewed appreciation that agriculture could play a significant role in the pursuit of Millennium Development Goals. In this context, the role of information dissemination through information and communication technology (ICT) in improving rural welfare is highlighted. However, some fear that with ICT technological disparity will arise, and existing socio-economic inequality and poverty will be further exacerbated. This study will use randomized experiment and surveys before and after the experiment to investigate the knowledge exchange impact of ICT on rural welfare in the Indian state of Karnataka. The randomized experiment or the action research proposed here involves facilitating information access on several essential services to households in some villages and not in others. Combining data from both surveys and the experiment, we investigate the knowledge exchange impact of information dissemination on household incomes, social network, risk coping mechanism and caste disparity.

In the research set-up, the agricultural information service is unique trial-based ‘seed-to-seed’ information delivered to farmers in their own farms by our specially trained and qualified extension advisors. The following are the key features of our DATES information service:
1. The agricultural extension advisor make visits to the fields of selected treatment farmers on a bi-monthly basis throughout the crop cycle. Each advisor is equipped with a unique IT/web-enabled handheld device – a tablet. These tablets contain a range of agricultural information with audio and video (animation of pest and diseases) that provides both written and spoken information in the local language. It also provides real time connectivity with the agricultural scientist in the local Agricultural Universities for help with new and undiagnosed pests and diseases. 2. The intervention provides wide-ranging information to farmers which are relevant for his business. The extension advisors visit the farms with the tablet that contains three main agricultural-based modules, such as: (i) nutrient management; (ii) plant protection; and (iii) crop agronomy. And, (iv) the DATES also covered information provision about agricultural credit such as government agricultural credit schemes, crop loans from banks and agricultural insurance information such as available government crop insurance schemes. .
3. In the nutrient management module, information on crop nutrition and soil nutrition is provided. In the initial stages of the intervention in 2013, majority of the farmers have had already applied fertilizer in their field. Hence, we had to wait until the end of the Kharif season to collect soil sample to test and advise the farmers on the nutritional requirements of their soil . For plant protection field issues, we used IT enabled interactive programme, named e-SAP (Electronic Solutions Against Agricultural Pests) that provide information to farmers in real time on pest-related problems on all the crops grown by our treatment farmers . The third module agronomy encompasses information on crop rotation, plant variety, irrigation and drainage, meteorology, and weed control. 4. Although, seven field crops are identified as the focus crop for the treatment in the study: Paddy, Ragi, Cotton, Redgram, Bengal gram, and Sunflower, we found during the field experience that there has been demand for information on various other field crops such as groundnut, pigeon pea, chili, sugarcane etc. For example, some selected farmers in the project sites: Siruguppa and Gubbi, cultivate groundnut and require information on that crop. Although we do not study the impact of DATES on all crops that a farmer grows, the project provided other demand based information.
5. If there is a problem in the crop, the extension agents will first try to diagnose it with the help of material in the tablet. Along with the diagnosis, the agents also suggest remedial actions with a paper printout of each prescription to the relevant treatment farmers. However, if he was not sure about the problem then he takes three photographs of the affected crop parts and field conditions from different angles and submits to the online server. The scientist at the back-end takes care of the issues. The solutions are uploaded in the server and the agent further communicates it to the farmer. Thus, DATES is a complete ‘seed-to-seed’ treatment that fosters conditions for inclusive productivity growth with the provision of real-time information on agricultural technology, solution and communication. It provides combination of interventions, including better technical advice on production process, especially on the use of variable inputs (including water), with the objectives of increasing the efficiency of the methods of production, encouraging the adoption of new technologies and providing integrated agricultural information service to monitor plant health.

Given the nature of intervention, randomization was carried out at the level of gram panchayat (GP). We followed the two stage procedure in the selection of samples for the intervention. In the first stage GP’s were randomly allocated to treatment and control groups, and in the second stage farm households were randomly selected. Two districts from different agro-climatic zones were purposively chosen to represent different soil types, diversity in crop variety and varied cultivating practices. And from each of the districts one taluk was selected – Gubbi taluk from Tumkur district and Siriguppa taluk from Bellary district.The reference population of the experiment are farmers growing field crops in two different agro-climatic zones - Gubbi taluk in Tumkur district and Siriguppa taluk in Bellary district.

Experimental Design Details

Randomization Method

Randomization was done in excel using office computer at the Indian Institute of Management Bangalore in India.
Step 1: Stratified GP; sorted them in A-Z order; gave serial no.
Step 2: Use "randbetween" function to draw 6 from CSC list.
Assign odd draws to "TREATMENT 1" group.
Assign even draws to "CONTROL" group. Discard a draw if a GP is chosen more than once.
Step 3: Use "randbetween" function to draw 6 from Non-CSC list.
Assign odd draws to "TREATMENT 2" group.
Assign even draws to "CONTROL" group. Discard a draw if a GP is chosen more than once.

Randomization Unit

Randomization was done at the gram panchayat (GP) level. In Gubbi, prior to randomization the gram panchayats were stratified depending on whether they had common services centre located within their boundaries. No stratification was followed for Siriguppa. Gubbi: One of the project objectives is to investigate the effectiveness of CSC in information dissemination. As CSC is a gram panchayat (GP) based operation, a two-stage RCT has been adopted for Gubbi to ensure appropriate representation of CSC in the experimental population.The GP population in Gubbi (N = 32) is first divided into two distinct sub-populations or strata and random samples are taken separately from each stratum. One stratum is GP with CSC (N1 = 15) and the other stratum GP without CSC (N2 = 17) implying that almost 50% GP have CSC. The number of GPs in a Taluka typically is around 30. We decided to select 12 GPs in each Taluka to ensure adequate representation of GPs in different categories such as CSC/Non-CSC and control and treatment groups. These 12 GPs will be equally split between control and treatment groups. If 50% representation of CSC in the experimental population is to be guaranteed then, six GP should be chosen from each stratum. Then at the first stage of randomization, within each stratum, selected GP (6) are split equally into control and treatment groups. Random number generation is done in Excel. To prevent flow of information from control GP to treatment GP and possible contamination of control farmers, a constraint is imposed on randomization viz. none of the control and treatment GP should be neighbors. We used Taluka level map with GP boundaries, to take care of this issue. After some trials, we got a sample which would suffice the constraint and we finished the process. Siruguppa: For Siruguppa we applied the same two-stage randomization procedure, leaving the stratification part at the beginning as it was not required. From 25 GP in Siruguppa, 12 are randomly chosen and then are split equally in treatment and control. Randomization is done in Excel. Similar condition is applied in random sampling: None of the control and treatment GP should be neighbours. In Siruguppa, we plan to have only one type of treatment viz. T2. Another interesting research question which can be addressed is the magnitude of spillover effect. It has been observed in the field that farmers often collect information from other farmers in the village. Thus, farmers may pass the information to others in the village and the recipients may benefit as well. To measure this indirect benefit, it is decided to take some additional farmers in each treatment GP. They will not receive any direct information from the project but they will be surveyed.

Was the treatment clustered?

No

Experiment Characteristics

Sample size: planned number of clusters

12 gram panchayats choosen from a total of 32 gram panchayats in Gubbi taluk in Tumkur district and 12 gram panchayats choosen from a total of 25 gram panchayats in Siriguppa taluk in Bellary district.

Sample size: planned number of observations

1320 farmers in total with 600 each in two different districts distributed equally between control and treatment group along with 120 additional farmers as spillover group equally distributed from both districts.

Sample size (or number of clusters) by treatment arms

50 farmers in 6 gram panchayats from two districts are controls (50X6X2) and another 50 farmers in 6 gram panchayats are treatment (50X6X2). In addition, 10 farmers from each treatment gram panchyats are spillover (10X12).

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

we have utilized two different data sets for sample size calculation: (i) our pilot survey; (ii) AIC crop cutting data. Assumptions behind sample size calculation are: (i) we want to achieve 10% increase in yield which is justified by yield gap noted by Krishi Vignana Kendra Frontline demonstration (KVK FLD); (ii) we want to achieve 80% power; (iii) 95% significance level. With a mean of 11.58 quintals per acre and standard deviation of 4.36 quintals per acre calculations show that sample size greater than 300 or more is satisfactory.